Organising for Quality and Value

Delivering Improvement Programme

Quality, Service Improvement and Redesign:

Practitioner Programme

Applying Quality Improvement to the Five

Year Forward View

Let me train you to be an Analyst….

Toss a coin….

What are the chances?

LCL

UCL

MEAN

7 Points above centre line

SPC rules – A run of Seven

A run of seven points all above or all below the centre

line, or all increasing or all decreasing

XX

X

X

XX

X

XX

XX

X

X

X

XX

X

XX

X

7 Points below centre line

0

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Falls Ulcers VTE UTI

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Type of Harm

Anywhere NHS Trust Harms (5 Months)

80% of

the

Harms 0

5

10

15

20

25

30

35

40

WardA

WardH

WardY

WardL

WardI

WardP

WardB

WardZ

WardQ

WardK

WardS

WardU

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Anywhere NHS Trust - Falls(5 Months)

The Pareto Principle

Combining SPC and Pareto…….

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Falls Ulcers VTE UTI

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Type of Harm

Anywhere NHS Trust Harms (5 Months)

80% of

the

Harms

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5

10

15

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25

30

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40

WardA

WardH

WardY

WardL

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Anywhere NHS Trust - Falls(5 Months)

Mtweek_2

Mtyear_2

1885114420105316649

20142014201320132013201220122011201120112010

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_X=29.05

UCL=52.83

LCL=5.27

1

1

5 COPD pathways - Spring and Winter Attenders (weekly)

The usual approach

Aims

measurements

change ideas

The Improvement Guide

Langley et al (1996)

What are we trying toaccomplish?

How will we know that achange is an improvement?

What changes can we make that will result in the improvements that we seek ?

Model for Improvement

Act Plan

Study Do

testing ideas before

implementing changes

The Five year view for Cancer….

Faster diagnosis. We need to take early action to reduce the

proportion of patients currently diagnosed through A&E—

currently about 25% of all diagnoses. These patients are far

less likely to survive a year than those who present at their GP

practice. Currently, the average GP will see fewer than eight

new patients with cancer each year, and may see a rare cancer

once in their career.

Better treatment and care for all. It is not enough to improve the

rates of diagnosis unless we also tackle the current variation in

treatment and outcomes.

Leydig tumors account for 1% of all testicular cancer

Question 1

What should we do first?

A) Set some targets. Set Cancer targets that seem sensible, that

stretch, but are achievable.

B) Write a clear aims statement, which does not contain any

solutions.

C) Create SPC charts.

D) Create Pareto charts.

Question 2

What’s next? After working up the Aim, what’s the next two tool

needed?

1) Cause and effect diagrams and SBAR

2) Driver diagrams and 6 thinking hats

3) Pareto and SPC

4) Spaghetti Diagrams and Fresh Eyes

Create Pareto charts to define and understand the

problem. Use SPC charts to see the 80%’s if they have

always been the problem, are growing or shrinking.

If I had an hour to save the

world, I Would Spend 55

Minutes Defining the Problem

and then Five Minutes Solving

It

(Einstein – Misquoted)

The Five Year Forward View

Outcomes vs Processes

The Donabedian Model

Outcome

Process

Balancing Measures

Creating measures

at all levels

System

Team

Service

Balance of Health

and Social Care

Outcomes

Line of sight across

the levels of

integrated care

• Type of measure by domain

• Timeline (baseline – 1, 3, 5 and 10 years)Adapted

from AquA

How capable are your processes of achieving

targets?

How capable are your processes of achieving targets?

Example - Door to needle times

75% of heart attack patients will receive thrombolysis within 20

minutes of their arrival in hospital

Imagine this is your process - Is it capable of

achieving 75% of patients treated in 20 minutes?

Door to Needle Times

0

10

20

30

40

50

60

70

80

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Consecutive patients

Min

ute

s

Door to needle time Average UCL LCL Target

Door to

needle

time Average UCL LCL

Moving

range

Average

MR Target

28 31 68 0 14 20

45 31 68 0 17 20

50 31 68 0 5 20

40 31 68 0 10 20

20 31 68 0 20 20

15 31 68 0 5 20

46 31 68 0 31 20

30 31 68 0 16 20

20 31 68 0 10 20

45 31 68 0 25 20

46 31 68 0 1 20

34 31 68 0 12 20

20 31 68 0 14 20

10 31 68 0 10 20

30 31 68 0 20 20

50 31 68 0 20 20

30 31 68 0 20 20

23 31 68 0 7 20

10 31 68 0 13 20

30 31 68 0 20 20

28 31 68 0 2 20

30 31 68 0 2 20

9 31 68 0 21 20

40 31 68 0 31 20

42 31 68 0 2 20

Capability = Target - Average

3 * sigma

Formula

Calculation (example)

Target 10 mins

Average 20

Sigma 18.7

10 - 20 =

3 x 18.7-0.18

TargetAverage

Sigma

Interpretation

Value Capability of Achieving Target

more than 1 100%

0-1 50-100%

less than 0 0-50%

How capable is the process of achieving the target?

Capability value % Capability Capability value % Capability

0 50 0.42 89.6

0.02 52.4 0.44 90.7

0.04 54.8 0.46 91.6

0.06 57.1 0.48 92.5

0.08 59.5 0.5 93.3

0.1 61.8 0.52 94.1

0.12 64.1 0.54 94.7

0.14 66.3 0.56 95.4

0.16 68.4 0.58 95.9

0.18 70.5 0.6 96.4

0.2 72.6 0.62 96.9

0.22 74.5 0.64 97.3

0.24 76.4 0.66 97.6

0.26 78.2 0.68 97.9

0.28 80 0.7 98.2

0.3 81.6 0.75 98.8

0.32 83.2 0.8 99.2

0.34 84.6 0.85 99.5

0.36 86 0.9 99.7

0.38 87.3 0.95 99.8

0.4 88.5 1 99.9

Table of Capability Values

Is your process Capable?

= 20 - 31 = -0.30

37The figure is negative so this process is not capable

of achieving 100% within 20 minutes.

A minus figure means more than 50% of patients will

not meet target.

The maximum this process can deliver is 18.4%and the target is 75%

Therefore, the process needs significantly

redesigning to achieve the target

Question 3

Before you can attempt capability calculations the data needs to meet

certain criteria?

A) No, You can do capability analysis on any data

B) Yes , The data needs to be “in control and stable”

Question 4

How do you know if your data is stable, predictable and in control?

A) All the points are inside of the UCL and LCL

B) There are no runs of 7 points

C) The distribution of the data is as expected

D) All of the above

Understanding GP

Capacity and Demand

252321191715131197531

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15

10

5

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Observation

Min

ute

s

_X=10.68

UCL=20.54

LCL=0.82

Patient Time (minutes) - GW

252321191715131197531

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15

10

5

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Observation

Min

ute

s

_X=9.48

UCL=20.56

LCL=-1.60

Patient Time (minutes) - SP

252321191715131197531

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20

15

10

5

0

Observation

Min

ute

s

_X=11.36

UCL=25.43

LCL=-2.71

Patient Time (minutes) - SF

252321191715131197531

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20

10

0

-10

Observation

Min

ute

s _X=11.64

UCL=29.70

LCL=-6.42

Patient Time (minutes) - JB

252321191715131197531

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40

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20

10

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Observation

Min

ute

s

_X=16.16

UCL=38.88

LCL=-6.56

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Patient Time (minutes) - SYA

4 are the same, one seems different…

SYAJBSPGWSF

20

18

16

14

12

10

8

Min

ute

s

Interval Plot of SF, GW, SP, JB, SYA95% CI for the Mean

SYA is

statistically

different from

the other 4

Activity, Backlog, Capacity & Demand…..

Demand:

All the requests

for a service –

from all sources

Capacity:

All that we can

do

Bottleneck:

Constraint is the

cause of the

bottleneckActivity:

What we actually

did

Backlog =

Queue =

Waiting List

Face to Face Appointments

13/0

6/20

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11/06/

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09/0

6/20

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03/0

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30/05/

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28/0

5/20

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26/05/20

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22/0

5/20

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20/05/20

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16/05/

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14/0

5/20

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12/05/

2014

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100

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Date

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Ap

pts

_X=129.2

UCL=267.1

LCL=-8.8

Number of face to face appts (daily)

24/1

1/20

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04/1

1/20

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15/1

0/20

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25/0

9/20

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05/09/

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18/08/

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29/07/

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09/07/

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19/06/

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30/05/

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12/05/

2014

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_X=92.8

UCL=148.5

LCL=37.1

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Number of Patients on triage (daily)

Patients who were Triaged

Demand for GP Triage, is it the

same each day?

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Boxplot of Number on triage

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Individual Value Plot of Number on triage vs Mtday

Monday is statistically

different from the

other days of the

week (the other 4 days

are the same as each

other)

Conclusions for planning C&D

(triage patients only)

You must plan for Mondays Separately

Tue, Wed, Thurs & Fri are the same

You cannot plan capacity for the whole year. It is affected by

seasonality (runs of 7 points on SPC charts)

– Possibly need more weeks to prove this

You should plan one system from Oct onwards (winter)

We only have data from May, so not sure when the seasonality ends

in the data) – We can guess on the weather, but the years worth of

triage data would be better

Planning C&D

Take the 80% time (1.5 sigma above the average) – Half way

between the average and the UCL. This is the time that it takes

each Doc to do an appointment:

– GW = 15 minutes

– SP = 15 minutes

– SF = 18 minutes

– JB = 20 minutes

– SYA = 27 minutes

Planning C&D part 2 – Demand 80%

Monday’s 80% Triage = 139 people each Monday

The rest of the week Triage = 105 people each day.

Then times 80% times by 80% volumes to work out the minutes you

require to meet demand…

– If SF did all the work..• 18 minutes X 139 people = 2502 minutes (41.7 hours each Monday)

• 18 minutes x 105 people = 1890 minutes (31.5 hours each Tue, Wed, Thu & Fri)

– Total for the week = 10,062 minutes per week (167.7 Hours a week)

This needs additional calculations

Not all triaged work is a face to face appointment in the GP practice

Some are ‘visits’

– Take 25 timings for each GP for visits, we can adjust the proportions

accordingly.

Same Day Appts

24/11/

2014

04/11/

2014

15/10/20

14

25/0

9/20

14

05/0

9/20

14

18/08/

2014

29/07/

2014

09/07/

2014

19/0

6/20

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30/0

5/20

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12/05/

2014

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_X=39.22

UCL=69.22

LCL=9.22

11

Same day appts (Daily)65432

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Mtday

Sa

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Boxplot of Same day appt

Monday and Tuesday are

peak days for requests for

Same day Appts, Wed,

thurs & Friday are about

the same. If the tails

overlap they are not

statistically different

Number of Visits

09/10/

2014

23/09/

2014

05/0

9/20

14

20/08/

2014

04/08/

2014

17/0

7/20

14

01/07/

2014

13/06/

2014

28/0

5/20

14

12/05/

2014

25

20

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10

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Date

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Vis

its

_X=11.9

UCL=25.60

LCL=-1.80

Number of visits (daily)

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Mtday

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Boxplot of number of visits

Monday is

statistically

higher than the

other 4 days of

the week

Question 5: Do you routinely apply

these techniques to your work?

a) No, I didn’t know the techniques existed

b) No, I know the techniques, but don’t have the time

c) Yes, to some extent

d) Yes all the time

Question 6: How many people need to be

‘experts’ in Quality Improvement in an NHS

organisation of 4000 staff?

a) 6 people

b) 63 people

c) 648 people

d) 2129 people

Calculate the square root of the total number of

people in your organisation. This is the number of QI

‘Expert’ that you need.

If I had an hour to save the world I

Would Spend 55 Minutes Defining the

Problem and then Five Minutes

Solving It

(Einstein – Misquoted)

In God we Trust, everybody else bring

data

(W Edwards Deming)